Abstract

Recent research suggests that colorization models have the capability of generating plausible color versions from grayscale images. In this paper, we investigate whether colorization prior to gender classification improves classification performance on the FERET grayscale face dataset. For this, we colorize the images using an existing Lab colorization model, both with and without class rebalancing, and our novel HSV colorization model without class rebalancing. Then we construct gender classification models on the grayscale and colorized datasets using a reduced GoogLeNet convolutional neural network. Several models are trained using different loss functions (cross entropy loss, hinge loss) and gradient optimization solvers (Nesterov's Accelerated Gradient Descent, Stochastic Gradient Descent), initialized using both random and pre-trained weights. Finally, we compare the gender classification accuracies of the models when applied to the face image color variants. The best performances are obtained by models initialized using pre-trained weights, and models using colorization without class rebalancing.